Physical Processes & Issues

23-25 February 2004 - Jean-Louis Brenguier - Météo-France

Cumulus Parameterization in the Context of Turbulence StudiesPhysical Processes & Issues

Aerosol

Droplets & Xtals

Cloud Drops & Ice

PrecipitationActivation

Condensation Collection

Evaporation

VIS IRRadiativeTransferClimate

AerosolProcessingAir quality Cloud

Dynamics

HydrologicalCycle


Cumulus Parameterization in the Context of Turbulence StudiesIssues

Hydrological Cycle• Turbulence and the onset of precipitation• Cloud Microphysics/Dynamics Feed-back• Impact of Aerosol on Precipitation Efficiency ()



Climate Studies• Cloud Radiative forcing (Nrv

3, Nrs2)

• Aerosol 1st Indirect Effect (Aerosol Activation & Cloud/Radiation)• Aerosol 2nd Indirect Effect (Cloud Life Time)




Climate Studies• Cloud Radiative forcing (Nrv

3, Nrs2)

• Aerosol 1st Indirect Effect (Aerosol Activation & Cloud/Radiation)• Aerosol 2nd Indirect Effect (Cloud Life Time)

Environment & Air Quality• Gas/Aerosol Interactions• Aerosol Processing in Clouds



Cumulus Parameterization in the Context of Turbulence Studies

Bulk thermodynamics : qc=qt-qvs(T) +0

Parameterization of the Microphysics



Bulk thermodynamics : qc=qt-qvs(T) +0Bulk microphysics : liquid precipitating qc , qr

qc , qr , Nc

qc , qr , Nc , Nr

mixed phase qc , qr , Nc , Nr , qi , qs , qg , …+4~10

+2+1

+3





qc , qr , Nc

qc , qr , Nc , Nr


+2+1

+3

Spectral microphysics : liquid precipitating fc(r)dr , fr(r)dr

mixed phase fx(r)dr +4p ~ 10p

+2p




Spectral microphysics + aerosol qc , Nc , ma

qc , Nc , ma , Na

diverse composition qc , Nc , ma,x , Na,x

diverse mixing states qc , Nc , ma,x,y , Na,x,y

Spectral coupled aerosol/droplet f(ma,rd)dma,drd

+1+2q

+3+2

+1+2ql

+pql


qc , qr , Nc

qc , qr , Nc , Nr


+2+1

+3

Spectral microphysics : liquid precipitating fc(r)dr , fr(r)dr

mixed phase fx(r)dr




For the most complete description of cloud microphysics, we should describe each aerosol & cloud particle, its location, velocity, temperature, chemical composition, surface properties, hygroscopic and optical properties, amount of water, its shape, complete address, bank account and social security number.

Sometimes, simplifications are welcome.

Question 1: which level of simplification ? Consistency with the whole model!

Question 2: is it that the physical process is not understood, even with the most detailed model ? or only a matter of simplifying the detailed model ?



Cumulus Parameterization in the Context of Turbulence StudiesHydrological Cycle , Climate Studies , Environment

Hydrological Cycle• Turbulence and the onset of precipitation

Small cumuli are precipitating faster than predicted !

Droplets are not randomly distributed in space (turbulent microstructure), some isolated priviledged droplets are lucky and grow more than droplets in crowded cloud suburbs !

Social science in cloud microphysics

Diffusional growth by vapour diffusion on a population of droplets randomly distributed in space is too slow for producing the few big droplets that will act as precipitation embryo !



Droplets are not randomly distributed in space (turbulent microstructure), some isolated priviledged droplets are lucky and grow more than droplets in crowded cloud volumes !

Chaumat & Brenguier JAS 2000



Small cumuli are precipitating faster than predicted !

Spatial heterogeneities may play a role, but not for enhancing the condensation process, possibly for the collection process !

Any experimental evidence of this assessment ?



Hydrological Cycle• Cloud Microphysics/Dynamics Feed-back• Impact of Aerosol on Precipitation Efficiency ()

() Aerosol particles impact cloud droplet and ice particle formation. This is well known since 50 years

Does that mean that there are modifications of the precipitation efficiency ?

50 years of weather modification programmes have not been sucessful to demonstrate such an assessment,

nor the sign of the change !



• Cloud Radiative forcing (Nrv3, Nrs

2)• Aerosol 1st Indirect Effect (Aerosol Activation & Cloud/Radiation)• Aerosol 2nd Indirect Effect (Cloud Life Time)

Hydrological Cycle , Climate Studies , Environment

Aerosol Activation:



Closure Experiment on the Activation Process

Comparison between measured CDNC (horizontal bars) and predictions based on- observed aerosol properties (black)- properties derived from measured CCN activation spectrum (red & green)

The CDNC prediction is overestimated by a factor of up to 2

0

200

400

600

CD

NC

, cm

-3

18-July

Snider et al. JGR 2003



Closure Experiment on the Activation ProcessIs that bias an obstacle ?

In BL clouds CDNC values in non-diluted (quasi-adiabatic) cloud cells varies

from 50% to 150 % of the mean Nact

qc(h)>0.9 qcad(h)

Ndrizzle< 2cm-3

0.4H < h < 0.6H

Pawlowska & Brenguier Tellus 2000




Droplet growth is well parameterized with the adiabatic model, with N=Nact

Nact=55 cm-3 Nact=244 cm-3

Droplet mean volume diameter versus height above cloud baseThe middle line is the adiabatic prediction with N=Nact

Pawlowska & Brenguier Tellus 2000






Aerosol Activation:

State-of-the-Art: Predictions are overestimated, most likely because the hygroscopic properties of the aerosol are not correctly accounted for in the process models. However simplified parameterizations exist that accurately replicate the detailed process models.

Objective: better description of aerosol properties to reach an accuracy of 50 %






Aerosol Activation:

Cloud radiative properties:




Radiative transfer retrievals are consistent with in situ observed CDNC

Reflectances VIS & NIR N retrieval versus Nact Predicted H-N Isolignes Schüller et al. JGR 2003






Aerosol Activation:


State-of-the-Art: Very accurate process models and parameterizations. Still minor biases most likely due to poor description of the cloud heterogeneity and of the aerosol absorbing properties

Objective: better description of aerosol absorption and impact of cloud heterogeneity to reach an accuracy of 5 %






Aerosol Activation:


Cloud Life Cycle:



Turbulent Fluxes

Entrainment-Mixing

rv=20 g kg-1

rl=0.2 g kg-1

PrecipitationEvaporation<1mm /day

A=0.05

A=0.50

Microphysics

Trv

Onset ofPrécipitation

1st 2ndand Aerosol Indirect Effect

CCN Activation

RadiativeTransfer



State of the art in GCM simulation of AIE

Menon et al. JGR 2004



State of the art in GCM simulation of AIE

Menon et al. JGR 2004



Parameterization of precipitation in GCM

Detailed microphysics 1 to 3-D (50 to 200 variables)

3-D CRM Runs (diverse conditions)Tripoli-Cotton, Beheng, Khairoutdinov-Kogan

Bulk microphysics for CRM (3 variables: N, qc, qr)

Auto-conversion (N, qc) and Accretion (N, qc, qr)

Tuning bulk coefficients to accountfor GCM grid smoothing effects

Bulk microphysics for GCM (2 variables : N, qc)

Auto-conversion (N, qc) (Accretion diagnosed)

3-D bulk CRM Runs (meso-scale)

Bulk microphysics for GCM (2 variables : N, H)

Average precipitation rate from multi-cells in stationary state



Precipitation for an ensemble of cloud cells:

Super-bulk parameterizations of precipitation

in BL clouds, using only N and H

Reduction rate of cloud water by precipitationas a power law of H and Nact

Pawlowska & Brenguier JGR 2003






Aerosol Activation:


Cloud Life Cycle:

State-of-the-Art: Very poor description of thin BL clouds in large scale models. Use of inadequate parameterizations that are only valid when local values of condensed water mixing ratios are prognosed.

Objective: better description of thin clouds & new super bulk parameterizations of precipitation



Back to basics: Model & Parameterizations Consistencydroplet formation and growth

How to translate the accuracy of a sophisticated 0D activation models into a multi-dimensionnal cloud model, without prognostic of supersaturation, and with a vertical resolution smaller than the height necessary for the activation process to be completed ??



Distribution dimensionnelle des

aérosols secs

Courbes de Köhler (8 noyaux de sulfate d’ammonium)

Diamètres secs :

0.043 m0.061 m0.079 m 0.102 m0.121 m0.144 m0.171 m0.203 m

0.043 m

0.203 m

Diamètre des gouttelettes de solution

Sursaturation de la parcelle

sourcepuitsursaturation parcelle Sp

S = Smax

S=-0.05%

sourcepuitSp

w = 0.4 m/s

Dry Diameter :0.102 m0.121 m




How to translate the accuracy of a sophisticated 0D spectral microphysical model into a 3-D cloud model, without prognostic of supersaturation, and with a horizontal resolution greater than the typical scale of the mixing processes??




Example: Radiative transfer in BL clouds

Bulk microphysics modelNrs

2 is derived from qc=Nrv3

by assuming eitherpure homogeneous (cst N)pure heterogeneous (cst rv)




HomogeneousN dilution onlyv evaporation

HeterogeneousN dilution and total evaporation of some dropletsv constant




Example: Radiative transfer in BL clouds

Pure homogeneous Pure heterogeneous Measured reflectance

=2.3 =1.7

The relative difference between hetero/homogeneous mixing schemes ( 30 %) is equivalent to the impact of a CDNC

increase by a factor of 2

Documents

Physical Processes & Issues